feat(honcho): async memory integration with prefetch pipeline and recallMode
Adds full Honcho memory integration to Hermes: - Session manager with async background writes, memory modes (honcho/hybrid/local), and dialectic prefetch for first-turn context warming - Agent integration: prefetch pipeline, tool surface gated by recallMode, system prompt context injection, SIGTERM/SIGINT flush handlers - CLI commands: setup, status, mode, tokens, peer, identity, migrate - recallMode setting (auto | context | tools) for A/B testing retrieval strategies - Session strategies: per-session, per-repo (git tree root), per-directory, global - Polymorphic memoryMode config: string shorthand or per-peer object overrides - 97 tests covering async writes, client config, session resolution, and memory modes
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Erosika
parent
8eefbef91c
commit
74c214e957
17 changed files with 2478 additions and 135 deletions
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@ -1640,6 +1640,25 @@ def _cleanup_old_recordings(max_age_hours=72):
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logger.debug("Recording cleanup error (non-critical): %s", e)
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def _cleanup_old_recordings(max_age_hours=72):
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"""Remove browser recordings older than max_age_hours to prevent disk bloat."""
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import time
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try:
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hermes_home = Path(os.environ.get("HERMES_HOME", Path.home() / ".hermes"))
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recordings_dir = hermes_home / "browser_recordings"
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if not recordings_dir.exists():
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return
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cutoff = time.time() - (max_age_hours * 3600)
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for f in recordings_dir.glob("session_*.webm"):
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try:
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if f.stat().st_mtime < cutoff:
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f.unlink()
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except Exception:
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pass
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except Exception:
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pass
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# ============================================================================
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# Cleanup and Management Functions
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# ============================================================================
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@ -1,8 +1,16 @@
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"""Honcho tool for querying user context via dialectic reasoning.
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"""Honcho tools for user context retrieval.
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Registers ``query_user_context`` -- an LLM-callable tool that asks Honcho
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about the current user's history, preferences, goals, and communication
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style. The session key is injected at runtime by the agent loop via
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Registers three complementary tools, ordered by capability:
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query_user_context — dialectic Q&A (LLM-powered, direct answers)
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honcho_search — semantic search (fast, no LLM, raw excerpts)
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honcho_profile — peer card (fast, no LLM, structured facts)
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Use query_user_context when you need Honcho to synthesize an answer.
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Use honcho_search or honcho_profile when you want raw data to reason
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over yourself.
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The session key is injected at runtime by the agent loop via
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``set_session_context()``.
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"""
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@ -34,54 +42,6 @@ def clear_session_context() -> None:
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_session_key = None
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# ── Tool schema ──
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HONCHO_TOOL_SCHEMA = {
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"name": "query_user_context",
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"description": (
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"Query Honcho to retrieve relevant context about the user based on their "
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"history and preferences. Use this when you need to understand the user's "
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"background, preferences, past interactions, or goals. This helps you "
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"personalize your responses and provide more relevant assistance."
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),
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"parameters": {
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": (
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"A natural language question about the user. Examples: "
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"'What are this user's main goals?', "
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"'What communication style does this user prefer?', "
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"'What topics has this user discussed recently?', "
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"'What is this user's technical expertise level?'"
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),
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}
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},
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"required": ["query"],
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},
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}
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# ── Tool handler ──
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def _handle_query_user_context(args: dict, **kw) -> str:
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"""Execute the Honcho context query."""
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query = args.get("query", "")
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if not query:
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return json.dumps({"error": "Missing required parameter: query"})
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if not _session_manager or not _session_key:
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return json.dumps({"error": "Honcho is not active for this session."})
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try:
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result = _session_manager.get_user_context(_session_key, query)
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return json.dumps({"result": result})
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except Exception as e:
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logger.error("Error querying Honcho user context: %s", e)
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return json.dumps({"error": f"Failed to query user context: {e}"})
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# ── Availability check ──
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def _check_honcho_available() -> bool:
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@ -89,14 +49,145 @@ def _check_honcho_available() -> bool:
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return _session_manager is not None and _session_key is not None
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# ── honcho_profile ──
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_PROFILE_SCHEMA = {
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"name": "honcho_profile",
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"description": (
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"Retrieve the user's peer card from Honcho — a curated list of key facts "
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"about them (name, role, preferences, communication style, patterns). "
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"Fast, no LLM reasoning, minimal cost. "
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"Use this at conversation start or when you need a quick factual snapshot. "
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"Use query_user_context instead when you need Honcho to synthesize an answer."
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),
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"parameters": {
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"type": "object",
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"properties": {},
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"required": [],
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},
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}
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def _handle_honcho_profile(args: dict, **kw) -> str:
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if not _session_manager or not _session_key:
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return json.dumps({"error": "Honcho is not active for this session."})
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try:
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card = _session_manager.get_peer_card(_session_key)
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if not card:
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return json.dumps({"result": "No profile facts available yet. The user's profile builds over time through conversations."})
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return json.dumps({"result": card})
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except Exception as e:
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logger.error("Error fetching Honcho peer card: %s", e)
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return json.dumps({"error": f"Failed to fetch profile: {e}"})
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# ── honcho_search ──
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_SEARCH_SCHEMA = {
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"name": "honcho_search",
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"description": (
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"Semantic search over Honcho's stored context about the user. "
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"Returns raw excerpts ranked by relevance to your query — no LLM synthesis. "
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"Cheaper and faster than query_user_context. "
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"Good when you want to find specific past facts and reason over them yourself. "
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"Use query_user_context when you need a direct synthesized answer."
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),
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"parameters": {
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "What to search for in Honcho's memory (e.g. 'programming languages', 'past projects', 'timezone').",
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},
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"max_tokens": {
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"type": "integer",
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"description": "Token budget for returned context (default 800, max 2000).",
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},
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},
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"required": ["query"],
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},
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}
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def _handle_honcho_search(args: dict, **kw) -> str:
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query = args.get("query", "")
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if not query:
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return json.dumps({"error": "Missing required parameter: query"})
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if not _session_manager or not _session_key:
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return json.dumps({"error": "Honcho is not active for this session."})
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max_tokens = min(int(args.get("max_tokens", 800)), 2000)
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try:
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result = _session_manager.search_context(_session_key, query, max_tokens=max_tokens)
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if not result:
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return json.dumps({"result": "No relevant context found."})
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return json.dumps({"result": result})
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except Exception as e:
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logger.error("Error searching Honcho context: %s", e)
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return json.dumps({"error": f"Failed to search context: {e}"})
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# ── query_user_context (dialectic — LLM-powered) ──
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_QUERY_SCHEMA = {
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"name": "query_user_context",
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"description": (
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"Ask Honcho a natural language question about the user and get a synthesized answer. "
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"Uses Honcho's LLM (dialectic reasoning) — higher cost than honcho_profile or honcho_search. "
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"Use this when you need a direct answer synthesized from the user's full history. "
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"Examples: 'What are this user's main goals?', 'How does this user prefer to communicate?', "
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"'What is this user's technical expertise level?'"
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),
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"parameters": {
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "A natural language question about the user.",
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}
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},
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"required": ["query"],
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},
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}
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def _handle_query_user_context(args: dict, **kw) -> str:
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query = args.get("query", "")
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if not query:
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return json.dumps({"error": "Missing required parameter: query"})
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if not _session_manager or not _session_key:
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return json.dumps({"error": "Honcho is not active for this session."})
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try:
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result = _session_manager.dialectic_query(_session_key, query)
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return json.dumps({"result": result or "No result from Honcho."})
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except Exception as e:
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logger.error("Error querying Honcho user context: %s", e)
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return json.dumps({"error": f"Failed to query user context: {e}"})
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# ── Registration ──
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from tools.registry import registry
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registry.register(
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name="honcho_profile",
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toolset="honcho",
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schema=_PROFILE_SCHEMA,
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handler=_handle_honcho_profile,
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check_fn=_check_honcho_available,
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)
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registry.register(
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name="honcho_search",
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toolset="honcho",
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schema=_SEARCH_SCHEMA,
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handler=_handle_honcho_search,
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check_fn=_check_honcho_available,
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)
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registry.register(
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name="query_user_context",
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toolset="honcho",
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schema=HONCHO_TOOL_SCHEMA,
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schema=_QUERY_SCHEMA,
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handler=_handle_query_user_context,
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check_fn=_check_honcho_available,
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)
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